Scripts to run CAVIAR, FINEMAP (version 1.1) and DAP-G

# knitr::opts_chunk$set(error = TRUE) # Knit even with errors
# devtools::install_local("echolocatoR/", force = T)
# library("echolocatoR")
source("echolocatoR/R/MAIN.R")
# MINERVA PATHS TO SUMMARY STATISTICS DATASETS: 
Data_dirs <- read.csv("./Data/directories_table.csv")
finemap_results <- list()
coloc_results <- list()

Nalls et. al. (2019) w/ 23andMe

Statistical Fine-mapping

Parkinson’s Disease GWAS.

dataset_name <- "Nalls23andMe_2019"
top_SNPs <- Nalls_top_SNPs <- import_topSNPs(
  topSS_path = Directory_info(Data_dirs, dataset_name, "topSumStats"),
  chrom_col = "CHR", position_col = "BP", snp_col="SNP",
  pval_col="P, all studies", effect_col="Beta, all studies", gene_col="Nearest Gene",
  caption= "Nalls et al. (2018) w/ 23andMe PD GWAS Summary Stats",
  group_by_locus = T, 
  locus_col = "Locus Number",
  remove_variants = "rs34637584")
# Manually add new SNP of interest
top_SNPs <- data.table::rbindlist(list(top_SNPs, 
                                      data.table::data.table(CHR=14,POS=55360203, SNP="rs3783642", 
                                                   P=0, Effect=1, Gene="ATG14", Locus=NA),
                                      data.table::data.table(CHR=12,POS=53797236, SNP="rs34656641",
                                                   P=0, Effect=1, Gene="SP1", Locus=NA),
                                      data.table::data.table(CHR=5,POS=126181862, SNP="rs959573",
                                                   P=0, Effect=1, Gene="LMNB1", Locus=NA),
                                      data.table::data.table(CHR=17,POS=40609405, SNP="rs9897702",
                                                   P=0, Effect=1, Gene="ATP6V0A1", Locus=NA)
                                      ))

gene_list <- unique(top_SNPs$Gene)

finemap_results[[dataset_name]] <- finemap_gene_list(top_SNPs,
                                               gene_list = unique(c("LRRK2", gene_list))[1:2],
                                               trim_gene_limits = c(T, rep(F,length(gene_list))),
                                               dataset_name = dataset_name,
                                               dataset_type = "GWAS",
                                               query_by ="coordinates",
                                               subset_path = "auto",
                                               finemap_method = c("SUSIE","FINEMAP"),
                                               #,"ABF","COJO"), 
                                               force_new_LD = F,  
                                               force_new_subset = F,
                                               force_new_finemap = F,
                 # file_path = Directory_info(dataset_name, "fullSumStats"),
                 fullSS_path = "./Data/GWAS/Nalls23andMe_2019/nallsEtAl2019_allSamples_allVariants.mod.txt",
                 chrom_col = "CHR", position_col = "POS", snp_col = "RSID",
                 pval_col = "p", effect_col = "beta", stderr_col = "se", 
                 freq_col = "freq", MAF_col = "calculate",
                 A1_col = "A1",
                 A2_col = "A2",
                 bp_distance = 500000*2,
                 download_reference = T, 
                 LD_reference = "1KG_Phase1",
                 superpopulation = "EUR", 
                 LD_block = F, 
                 min_MAF = 0,
                 remove_variants = c("rs34637584"),
                 remove_correlates = c("rs34637584"),
                 conditioned_snps = "auto", # Use the lead SNP for each gene 
                 n_causal = 5, 
                 remove_tmps = T)

## ^^^^^^^^^ Running echolocatoR on: LRRK2 ^^^^^^^^^

LRRK2

[1] "" [1] “—————— Step 1: Query —————” [1] “+ Importing previous Multi-finemap file…Importing summary stats.” [1] “Subset file looks good! :)” [1] “+ Removing subset tmp…” [1] “Removing specified variants: rs34637584” [1] "" [1] “— Step 2: Calculate Linkage Disequilibrium —” [1] “+ Previously computed LD matrix detected. Importing… Data/GWAS/Nalls23andMe_2019/LRRK2/plink/LD_matrix.RData” [1] "" [1] “————– Step 3: Filter SNPs ————-” [1] “^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^——– Step 4: Statistically Fine-map ——–^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^” [1] “+++ Previously multi-fine-mapped results identified. Importing…” [1] “+ Fine-mapping with ‘SUSIE, FINEMAP’ completed in 0 seconds.” [1] "" [1] “————— Step 7: Visualize ————–” [1] “Plotting… original” [1] “+ Constructing SNP labels…” [1] “Plotting… SUSIE” [1] “+ Constructing SNP labels…” [1] “Plotting… FINEMAP” [1] “+ Constructing SNP labels…” [1] “+ LRRK2 Credible Set SNPs”

[1] “Generating summary table…” [1] “LRRK2 fine-mapped in 19.52 seconds”

## ^^^^^^^^^ Running echolocatoR on: ASXL3 ^^^^^^^^^

ASXL3

[1] "" [1] “—————— Step 1: Query —————” [1] “+ Importing previous Multi-finemap file…Importing summary stats.” [1] “Subset file looks good! :)” [1] “+ Removing subset tmp…” [1] “Removing specified variants: rs34637584” [1] "" [1] “— Step 2: Calculate Linkage Disequilibrium —” [1] “+ Previously computed LD matrix detected. Importing… Data/GWAS/Nalls23andMe_2019/ASXL3/plink/LD_matrix.RData” [1] "" [1] “————– Step 3: Filter SNPs ————-” [1] “^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^——– Step 4: Statistically Fine-map ——–^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^” [1] “+++ Previously multi-fine-mapped results identified. Importing…” [1] “+ Fine-mapping with ‘SUSIE, FINEMAP’ completed in 0.01 seconds.” [1] "" [1] “————— Step 7: Visualize ————–” [1] “Plotting… original” [1] “+ Constructing SNP labels…” [1] “Plotting… SUSIE” [1] “+ Constructing SNP labels…” [1] “Plotting… FINEMAP” [1] “+ Constructing SNP labels…” [1] “+ ASXL3 Credible Set SNPs”

[1] “Generating summary table…” [1] “ASXL3 fine-mapped in 3.76 seconds”

# 
# dat <- fread("Data/GWAS/Nalls23andMe_2019/ATP6V0A1/FINEMAP/data.snp")
# dat2 <- fread("Data/GWAS/Nalls23andMe_2019/ATP6V0A1/Multi-finemap/Multi-finemap_results.txt")
# dat2$SUSIE.Probability %>% unique()

Summarize Fine-mapping Results

Gather Annotations

merged_results <- merge_finemapping_results(minimum_support=1,
                                            include_leadSNPs=T,
                                            xlsx_path="./Data/annotated_finemapping_results.xlsx",
                                            from_storage=T,
                                            haploreg_annotation=T,
                                            biomart_annotation=T, 
                                            verbose = F)

## [1] "++ Submitting chunk 1 / 1"

## Note: zip::zip() is deprecated, please use zip::zipr() instead

createDT(subset(merged_results, Support == 2))

Epigenetics

NOTE: Must run ‘merge_finemapping_results’ with haploreg_annotation=T for this to work.

# Summarize epigenetics 
## Across all datasets and loci
epi_table <- epigenetics_summary(merged_results,
                     tissue_list = c("BRN","BLD"),
                     epigenetic_variables = c("Promoter_histone_marks","Enhancer_histone_marks"))

##                        Hits Total_SNPs Percent_Total
## Promoter_histone_marks 215  1297       16.58        
## Enhancer_histone_marks 548  1297       42.25

createDT(epi_table)

## Within LRRK2
### Enrichment 
LRRK2_dat <- data.table::fread("Data/GWAS/Nalls23andMe_2019/LRRK2/Multi-finemap/Multi-finemap_results.txt",
                        sep="\t", stringsAsFactors = F) 
epigenetics_enrichment(snp_list1 = subset(LRRK2_dat, Support >= 1)$SNP %>% unique(), 
                       snp_list2 = LRRK2_dat$SNP %>% unique(), 
                       chisq = T, 
                       fisher = T, 
                       tissue_list = c("BRN","BLD"),
                       epigenetic_variables = c("Promoter_histone_marks","Enhancer_histone_marks"))

## [1] "Conducting SNP epigenomic annotation enrichment tests..."
## [1] "+++ SNP list 1 :"
## [1] "+ Gathering annotation data from HaploReg..."
## [1] "++ Submitting chunk 1 / 1"
##                        Hits Total_SNPs Percent_Total
## Promoter_histone_marks 3    6          50           
## Enhancer_histone_marks 4    6          66.67        
## [1] "+++ SNP list 2 :"
## [1] "+ Gathering annotation data from HaploReg..."
## [1] "++ Submitting chunk 1 / 1"
##                        Hits Total_SNPs Percent_Total
## Promoter_histone_marks 48   560        8.57         
## Enhancer_histone_marks 141  560        25.18        
## [1] "++ Testing for enrichment of ' Promoter_histone_marks ' in the tissues ' BRN & BLD '"
## 
##  Pearson's Chi-squared test with simulated p-value (based on 2000
##  replicates)
## 
## data:  cont_tab
## X-squared = 7.5689, df = NA, p-value = 0.03198
## 
## 
##  Fisher's Exact Test for Count Data
## 
## data:  cont_tab
## p-value = 0.03134
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##   0.9106592 28.2085725
## sample estimates:
## odds ratio 
##   5.801191 
## 
## [1] "++ Testing for enrichment of ' Enhancer_histone_marks ' in the tissues ' BRN & BLD '"
## 
##  Pearson's Chi-squared test with simulated p-value (based on 2000
##  replicates)
## 
## data:  cont_tab
## X-squared = 2.4016, df = NA, p-value = 0.2429
## 
## 
##  Fisher's Exact Test for Count Data
## 
## data:  cont_tab
## p-value = 0.1264
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##   0.5411402 11.3177243
## sample estimates:
## odds ratio 
##   2.643166

Mutation Types

potential_missense <- SNPs_by_mutation_type(merged_results, mutation_type="missense_variant")
createDT(potential_missense)

candidate_counts <- counts_summary(top_SNPs, merged_results)

## [1] "\n + All 2 models converged upon 1 to 9 SNPs in 56.63 % of loci."

createDT(candidate_counts)

# Distribution of mutation types in Credible Set SNPs 
mod <- merged_results %>% 
  mutate(CredSet.Lead = 
           ifelse(Support>0,
                  ifelse(leadSNP==T,"CS & leadSNP", "CS & non-leadSNP"),
                  ifelse(leadSNP==T, "Non-CS & leadSNP","Non-CS & non-leadSNP")),NA) 
ggplot(data=mod, aes(x=consequence_type_tv, fill=CredSet.Lead)) + 
  geom_bar(aes(y = ..count..), position="dodge") + # (..count..)/sum(..count..)
  coord_flip() +
  labs(title="Proportions of Mutation Types (Biomart)",
       subtitle = "Support = Within Credible Set",
       x = "Mutation Type",
       y="SNP Count") + 
  theme(legend.text = element_text(size=8))

eQTL Boxplots: Fairfax

# snp_list <- subset(merged_results, Gene=="LRRK2")$SNP %>% unique()
snp_list <- c("rs7294519", "rs11175620", "rs76904798")

merged_eQTL_data <- eQTL_boxplots(snp_list,
             eQTL_SS_paths = file.path("Data/eQTL/Fairfax_2014",
                                       c("CD14/LRRK2/LRRK2_Fairfax_CD14.txt",
                                         "IFN/LRRK2/LRRK2_Fairfax_IFN.txt",
                                         "LPS2/LRRK2/LRRK2_Fairfax_LPS2.txt",
                                         "LPS24/LRRK2/LRRK2_Fairfax_LPS24.txt")),
             expression_paths = file.path("Data/eQTL/Fairfax_2014",
                                          c("CD14/CD14.47231.414.b.txt",
                                            "IFN/IFN.47231.367.b.txt",
                                            "LPS2/LPS2.47231.261.b.txt",
                                            "LPS24/LPS24.47231.322.b.txt")),
             genotype_path = "Data/eQTL/Fairfax_2014/geno.subset.txt",
             probe_path = "Data/eQTL/Fairfax_2014/gene.ILMN.map",
             .fam_path = "Data/eQTL/Fairfax_2014/volunteers_421.fam",
             gene = "LRRK2",
             show_plot = T,
             SS_annotations = F,
             interact = F)

## [1] ""
## [1] "+ Processsing Expression data"
## [1] "++ Extracting probe info"
## [1] "++ CD14"
## [1] "++ IFN"
## [1] "++ LPS2"
## [1] "++ LPS24"
## [1] ""
## [1] "+ Processing Summary Stats data"
## [1] ""
## [1] "+ Processing Genotype data"

## Warning in melt.data.table(., geno_subset, id.vars = c("CHR", "SNP",
## "POS", : 'measure.vars' [289, 290, 292, 293, ...] are not all of the same
## type. By order of hierarchy, the molten data value column will be of type
## 'double'. All measure variables not of type 'double' will be coerced too.
## Check DETAILS in ?melt.data.table for more on coercion.

## [1] ""
## [1] "+ Merging Summary Stats, Genotype, and Expression data"
## [1] ""
## [1] "+ Plotting eQTLs"

Colocalization: MESA

subpops <- c("CAU","AFA","HIS")
dataset2_pathList <- paste0("./Data/eQTL/MESA_",subpops,"/LRRK2_",subpops,"_MESA.txt")

colocalize_geneList(gene_list = c("LRRK2"), 
                    dataset1_path = "./Data/GWAS/Nalls23andMe_2019/LRRK2/Multi-finemap/Multi-finemap_results.txt",
                    dataset2_pathList = dataset2_pathList,
                    dataset1_type = "cc",
                    dataset2_type = "quant",
                    shared_MAF = 1, 
                    PP_threshold = .8, 
                    save_results = T, 
                    show_plot = T)

colocalize_geneList <- function(gene_list, 
                                dataset1_path, 
                                dataset2_pathList, 
                                dataset1_type="cc",
                                dataset2_type = "quant",
                                shared_MAF=1,
                                PP_threshold=0.8,
                                save_results=T,
                                show_plot=T,
                                chrom_col = "chr", 
                                position_col = "pos_snps", 
                                snp_col="snps",
                                pval_col="pvalue", 
                                effect_col="beta", 
                                gene_col="gene_name",
                                stderr_col = "calculate",  
                                tstat_col = "statistic"){
  for(g in gene_list){
    cat('\n')
    cat("###", g, "\n") 
    for(d2 in dataset2_pathList){
      comparison_name <- paste0(basename(dataset1_path),".vs.",basename(d2))  
      subset_DT <- extract_SNP_subset(gene = gene, 
                    top_SNPs = top_SNPs, 
                    fullSS_path = fullSS_path,
                    subset_path  =  subset_path,
                    force_new_subset = force_new_subset,
                    
                    chrom_col = chrom_col, 
                    position_col = position_col, 
                    snp_col = snp_col,
                    pval_col = pval_col, 
                    effect_col = effect_col, 
                    stderr_col = stderr_col,
                    gene_col = gene_col, 
                    tstat_col = tstat_col,
                    MAF_col = MAF_col,
                    freq_col = freq_col,
                    A1_col = A1_col,
                    A2_col = A2_col,
                    
                    bp_distance = bp_distance,
                    superpopulation = superpopulation,  
                    min_POS = min_POS, 
                    max_POS = max_POS, 
                    file_sep = file_sep, 
                    query_by = query_by,
                    probe_path = probe_path
                    ) 
      
      coloc_DT <- COLOC(
          gene = g,
          dataset1_path = dataset1_path, 
          dataset2_path = d2,
          dataset1_type = dataset1_type,
          dataset2_type = dataset2_type,
          shared_MAF = shared_MAF,
          PP_threshold = PP_threshold, 
          save_results = save_results,
          show_plot = show_plot, 
          )
      coloc_DT <- cbind(Comparison = comparison_name)
      coloc_results[[dataset_name]]
    } 
  cat("\n")
  } 
  return(coloc_results %>% data.table::rbindlist())
}

Colocalization: Fairfax

Summarize

gather_ff_data <- function(gene_snp_df){
  gene_list <- unique(gene_snp_df$Gene)
  # Loop through genes
  ff_summary <- lapply(gene_list, function(gene, gene_snp_df.=gene_snp_df){
    printer("+ Gathering Fairfax eQTLS for LRRK2")
    bp <- subset(gene_snp_df., Gene==gene)$POS[1]
    # Loop through eQTL conditions
    dat <- lapply(c("CD14","IFN","LPS2","LPS24"), function(condition, bp.=bp, gene.=gene){
      printer("+",condition)
      ff <- data.table::fread(file.path("./Data/eQTL/Fairfax_2014",condition,gene.,
                                        paste0(gene.,"_Fairfax_",condition,".txt")), sep="\t") %>% 
        subset(POS==bp.) %>% 
        data.table::as.data.table() %>% 
        tibble::add_column(Condition=condition, .before =1) %>% 
        dplyr::rename(PROBE_ID=gene)
      return(ff)
  }) %>% data.table::rbindlist() 
    return(dat %>% tibble::add_column(Gene=gene, .before =1))
}) %>% data.table::rbindlist() 
  return(ff_summary)
}

gene_snp_df <- subset(merged_results, Gene=="LRRK2" & Support==2, 
                      select = c("Gene","SNP","CHR","POS"))  %>% unique()
createDT(gather_ff_data(gene_snp_df))

## [1] "+ Gathering Fairfax eQTLS for LRRK2"
## [1] "+ CD14"
## [1] "+ IFN"
## [1] "+ LPS2"
## [1] "+ LPS24"

Fine-mapping eQTLs

AFR = AFA = African
AMR = Ad Mixed American
EAS = East Asian
EUR = CAU = European
SAS = South Asian
HIS = Hispanic

MESA

gene_list <- c("LRRK2") 
subpops <- c("CAU","AFA","HIS")  

for(g in gene_list){
  for(s in subpops){
  dataset_name <- paste0("MESA_",s)
  finemap_results[[dataset_name]] <- finemap_gene_list( 
                                                top_SNPs = Nalls_top_SNPs, 
                                                gene_list=c("LRRK2"),  
                                                superpopulation = s, 
                                                dataset_name = dataset_name, 
                                                finemap_method = c("SUSIE"),#c("SUSIE","ABF","FINEMAP"),
                                                force_new_LD = T,
                                                force_new_finemap = T, 
                                                force_new_subset = T, 
                                                dataset_type = "eQTL",
                                                query_by = "fullSS", 
                                                file_sep = "\t",
                   # fullSS_path = Directory_info(dataset_name, "fullSumStats"),
                   fullSS_path = paste0("./Data/eQTL/MESA_",s,"/",g,"_MESA_",s,".txt"),
                   subset_path = "auto",
                   chrom_col = "chr", position_col = "pos_snps", snp_col="snps",
                   pval_col="pvalue", effect_col="beta", gene_col="gene_name", 
                   stderr_col = "calculate",  tstat_col = "statistic",
                   n_causal = 5,   
                   download_reference = T,
                   remove_tmps = F) 
  }  
}

Fairfax (2014)

Use the Nalls et al. (2019) LRRK2 locus to query for SNPs in the eQTL data.
SS Files are SPACE-delimited (not tab-delimited)

# +++++++++++++++ CD14 Stimulation +++++++++++++++ #
dataset_name <- "Fairfax_2014_CD14"
allResults[[dataset_name]] <- finemap_gene_list( gene_list=c("LRRK2"),  
                                                superpopulation = "CAU",
                                                dataset_name = dataset_name, 
                                                dataset_type = "eQTL",
                                                top_SNPs = Nalls_top_SNPs, 
                                                query_by = "probes",
                                                probe_path = "./Data/eQTL/gene.ILMN.map",
                   file_sep = " ",                            
                   # fullSS_path = Directory_info(Data_dirs, dataset_name, "fullSumStats"),
                   fullSS_path = "./Data/eQTL/Fairfax_2014_CD14/LRRK2_Fairfax_CD14.txt",
                   subset_path = "auto",
                   chrom_col = "CHR", position_col = "POS", snp_col="SNP",
                   pval_col="p-value", effect_col="beta", gene_col="gene", 
                   stderr_col = "calculate",  tstat_col = "t-stat",
                   vcf_folder = vcf_folder, n_causal = 1, 
                   force_new_subset = force_new_subset,
                   LD_block = T)

Session Information

sessionInfo()

## R version 3.6.0 (2019-04-26)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Mojave 10.14.5
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] susieR_0.8.1.0521  GeneOverlap_1.20.0 haploR_3.0.1      
##  [4] XGR_1.1.5          dnet_1.1.4         supraHex_1.22.0   
##  [7] hexbin_1.27.3      igraph_1.2.4.1     coloc_3.2-1       
## [10] snpStats_1.34.0    Matrix_1.2-17      survival_2.44-1.1 
## [13] biomaRt_2.40.0     BiocManager_1.30.4 tidyr_0.8.3       
## [16] gaston_1.5.5       RcppParallel_4.4.3 Rcpp_1.0.1        
## [19] curl_3.3           ggrepel_0.8.1      cowplot_0.9.4     
## [22] plotly_4.9.0       ggplot2_3.2.0      dplyr_0.8.1       
## [25] data.table_1.12.2  readxl_1.3.1       usethis_1.5.0     
## [28] devtools_2.0.2     R.utils_2.9.0      R.oo_1.22.0       
## [31] R.methodsS3_1.7.1 
## 
## loaded via a namespace (and not attached):
##   [1] backports_1.1.4        RCircos_1.2.1          plyr_1.8.4            
##   [4] lazyeval_0.2.2         splines_3.6.0          crosstalk_1.0.0       
##   [7] wavethresh_4.6.8       GenomeInfoDb_1.20.0    ggnetwork_0.5.1       
##  [10] inline_0.3.15          digest_0.6.19          htmltools_0.3.6       
##  [13] gdata_2.18.0           magrittr_1.5           memoise_1.1.0         
##  [16] cluster_2.1.0          openxlsx_4.1.0.1       remotes_2.1.0         
##  [19] sna_2.4                prettyunits_1.0.2      colorspace_1.4-1      
##  [22] blob_1.1.1             rrcov_1.4-7            xfun_0.8              
##  [25] callr_3.2.0            crayon_1.3.4           RCurl_1.95-4.12       
##  [28] jsonlite_1.6           graph_1.62.0           ape_5.3               
##  [31] glue_1.3.1             gtable_0.3.0           zlibbioc_1.30.0       
##  [34] XVector_0.24.0         pkgbuild_1.0.3         Rgraphviz_2.28.0      
##  [37] BiocGenerics_0.30.0    DEoptimR_1.0-8         scales_1.0.0          
##  [40] mvtnorm_1.0-11         DBI_1.0.0              viridisLite_0.3.0     
##  [43] xtable_1.8-4           progress_1.2.2         bit_1.1-14            
##  [46] stats4_3.6.0           DT_0.7                 htmlwidgets_1.3       
##  [49] httr_1.4.0             gplots_3.0.1.1         RColorBrewer_1.1-2    
##  [52] pkgconfig_2.0.2        reshape_0.8.8          XML_3.98-1.20         
##  [55] reshape2_1.4.3         tidyselect_0.2.5       labeling_0.3          
##  [58] rlang_0.4.0            later_0.8.0            AnnotationDbi_1.46.0  
##  [61] munsell_0.5.0          cellranger_1.1.0       tools_3.6.0           
##  [64] cli_1.1.0              RSQLite_2.1.1          statnet.common_4.3.0  
##  [67] evaluate_0.14          stringr_1.4.0          yaml_2.2.0            
##  [70] processx_3.3.1         knitr_1.23             bit64_0.9-7           
##  [73] fs_1.3.1               zip_2.0.2              robustbase_0.93-5     
##  [76] caTools_1.17.1.2       purrr_0.3.2            nlme_3.1-140          
##  [79] mime_0.7               leaps_3.0              compiler_3.6.0        
##  [82] tibble_2.1.3           pcaPP_1.9-73           stringi_1.4.3         
##  [85] ps_1.3.0               desc_1.2.0             lattice_0.20-38       
##  [88] pillar_1.4.1           RUnit_0.4.32           bitops_1.0-6          
##  [91] httpuv_1.5.1           GenomicRanges_1.36.0   R6_2.4.0              
##  [94] promises_1.0.1         network_1.15           KernSmooth_2.23-15    
##  [97] IRanges_2.18.1         sessioninfo_1.1.1      MASS_7.3-51.4         
## [100] gtools_3.8.1           assertthat_0.2.1       pkgload_1.0.2         
## [103] BMA_3.18.9             rprojroot_1.3-2        withr_2.1.2           
## [106] S4Vectors_0.22.0       GenomeInfoDbData_1.2.1 parallel_3.6.0        
## [109] hms_0.4.2              grid_3.6.0             coda_0.19-2           
## [112] rmarkdown_1.13         Biobase_2.44.0         shiny_1.3.2

print(paste("susieR ", packageVersion("susieR")))

## [1] "susieR  0.8.1.521"

Fine Mapping:
Parkinson’s Disease

Fine Mapping:

Brian M. Schilder, Bioinformatician II
Raj Lab
Department of Neuroscience
Icahn School of Medicine at Mount Sinai
NYC, New York

RAJ LAB

Brian M. Schilder, Bioinformatician II
Raj Lab
Department of Neuroscience
Icahn School of Medicine at Mount Sinai
NYC, New York

Most Recent Update:
2019-06-28

Nalls et. al. (2019) w/ 23andMe

Statistical Fine-mapping

LRRK2

ASXL3

Summarize Fine-mapping Results

Gather Annotations

Epigenetics

Mutation Types

eQTL Boxplots: Fairfax

Colocalization: MESA

Colocalization: Fairfax

Summarize

Fine-mapping eQTLs

MESA

Fairfax (2014)

Session Information

Fine Mapping: Parkinson’s Disease

Fine Mapping:

Brian M. Schilder, Bioinformatician II Raj Lab Department of Neuroscience Icahn School of Medicine at Mount Sinai NYC, New York RAJ LAB

Brian M. Schilder, Bioinformatician II Raj Lab Department of Neuroscience Icahn School of Medicine at Mount Sinai NYC, New York

Most Recent Update: 2019-06-28

Nalls et. al. (2019) w/ 23andMe

Statistical Fine-mapping

LRRK2

ASXL3

Summarize Fine-mapping Results

Gather Annotations

Epigenetics

Mutation Types

eQTL Boxplots: Fairfax

Colocalization: MESA

Colocalization: Fairfax

Summarize

Fine-mapping eQTLs

MESA

Fairfax (2014)

Session Information

Fine Mapping:
Parkinson’s Disease

Brian M. Schilder, Bioinformatician II
Raj Lab
Department of Neuroscience
Icahn School of Medicine at Mount Sinai
NYC, New York

RAJ LAB

Brian M. Schilder, Bioinformatician II
Raj Lab
Department of Neuroscience
Icahn School of Medicine at Mount Sinai
NYC, New York

Most Recent Update:
2019-06-28